A. Field of the Invention
The present invention pertains generally to the analysis of spectral data and more particularly to assessing the health of plants through the use of spectral data that detects and analyzes the chlorophyll content in plant leaves.
B. Description of the Background
Determining and predicting crop health has been a goal of growers for centuries. As science has progressed, methods of assessing crop health have improved. In addition, as technology has advanced, new methods to assess crop health have arisen. With the advent of imaging technology, a variety of vegetation indices have been developed. Currently used vegetation indices are a function of vegetation cover, plant density, and plant health. However, vegetation indices may not accurately represent plant chlorophyll content or plant health. Moreover, vegetation indices are also a function of noise such as soil type, viewing angle of the imaging device, atmospheric conditions, the incident angle of the light source, and other background absorbers. Thus, vegetation indices only generally represent these properties. This severely undermines the usefulness of vegetation indicies, automated analysis and detection of a change in plant health.
In assessing vegetation indices, the ultimate goal is to find a methodology capable of deriving plant biophysical properties in a way which is:
representative of the plant physiology and, in some cases, pathology
robust against variations in soil background, plant type, environmental conditions, and geographic location
normalized for automated analyses
The best known vegetation index is called the Normalized Difference Vegetation Index (NDVI) and is computed as follows:
NDVI=[NIRxe2x88x92Red]/[NIR+Red]xe2x80x83xe2x80x83(1)
NIR is the near-infrared band that is detected by a sensor or camera system. Red is the red band detected by another sensor or camera system. NDVI is dimensionless and is normalized to have values between xe2x88x921 and 1. The physical interpretation of NDVI is simple. NDVI less than 0 may be attributed to features other than vegetation. These objects may be, for example, man-made features or water bodies. Values of NDVI greater than 0 may be attributed to vegetation, with higher values indicative of greater health and/or vegetation cover. However, NDVI is also a function of noise that can result from soil type, viewing angle of the imaging device, atmospheric conditions, the incident angle of the light source, and other background absorbers. Therefore, NDVI is only of marginal utility in assessing plant health. Moreover, NDVI does not allow for discrimination between plant health and a decrease in vegetation cover.
There are numerous derivative measures of NDVI designed to be somewhat less sensitive to noise. One of these is the Soil Adjusted Vegetation Index (SAVI). SAVI is computed as follows:
SAVI=[NDVI*L]/[1+L].xe2x80x83xe2x80x83(2)
where L is representative of the fractional vegetation cover. A value of L=0.5 is considered to be the most generic case. L may also be derived from a training set, e.g., a soil line, in which case the index is then called Modified SAVI (MSAVI), or from an initial soil image in which case the index is then called MSAVI2. However, since MSAVI and MSAV12 are derivative measures of NDVI, they still suffer from many of the same limitations as NDVI.
As shown in equations (1) and (2), vegetation indices exploit the differences in the reflectance response between the red and NIR bands to extract plant information. As used herein, reflectance and absorption are used within this document, such that, reflectance refers to the amount of light reflected by a particular substance, while absorption refers, inversely, to the amount of light absorbed by a particular substance. Differences in reflectance of a plant at certain frequencies has been interpreted as indicative of chlorophyll content and plant health. However, the near-infrared reflectance is substantially invariant to chlorophyll content, so that the near-infrared information is not indicative of chlorophyll content or plant health. Moreover, the reflectance in the red band saturates quickly (decreases in sensitivity) with increasing chlorophyll content. Hence, any correlation that NDVI and its derivatives may have with chlorophyll content and plant health using the red band, is insensitive to small variations in chlorophyll content and plant health.
It would therefore be useful to have a measure that assesses chlorophyll content and plant health. Specifically, it would be desirable to have a single measure that discriminates between plant health and vegetation cover, that is insensitive to noise, that is normalized for automated analysis, and is sensitive to small changes in chlorophyll content. It is against this background and these problems and limitations that the present invention has been developed.
The present invention overcomes the disadvantages and limitations of the prior art by providing a chlorophyll based health map that is generally not affected by background data.
The present invention may therefore comprise a method for generating a chlorophyll based health map for a geographic area comprising the steps of: obtaining a multi-spectral image data set for the geographical area, the multi-spectral image data set including at least one spectra that responds to chlorophyll and at least two spectra which respond to background data; removing the background data from said multi-spectral image data set to create a chlorophyll absorption data set; and determining an amount of chlorophyll in the geographic region represented by the chlorophyll absorption data set.
The advantages of the present invention are that a chlorophyll based health map is capable of providing early detection of plant health due to the fact that the chlorophyll based health map is relatively insensitive to changes in background data and has great sensitivity to changes in certain chlorophyll concentrations. Additionally, the present invention has many applications in areas such as forestry, environmental monitoring and assessment and resource exploration.